Knowledge is unevenly distributed in society and difficult to relocate and trans-fer. Furthermore, the locus of innovation has shifted from organizations to knowledge. Users are the source of novel needs and knowledge regarding them.
Thus, users could potentially produce more novel innovations. (Lakhani & Pan-etta, 2007; Sawhney & Prandelli, 2000). Moreover, the boundaries of innovations have become less bounded, and innovation outcomes often remain fluid and incomplete. Digital transformation and the characteristics of digital technology are increasingly scattering innovation landscape and speeding up digital inno-vation. Digital platforms and infrastructures are in focus of distributed and dig-ital innovation research. Digdig-ital technology and distributed innovation are highlighted as current IS research topics and connected with themes such as digital platforms. (Nambisan et al., 2017). The trend over the recent decades has been towards more decentralized and flexible research and development sys-tems (Howells, James & Malik, 2003).
Companies need to gain access and exploit external knowledge and tech-nologies to remain competitive (Howells, James & Malik, 2003). In addition, firms are specializing and focusing on a narrow scope of knowledge to compete in technology market. It requires collaboration with partners and customers to create knowledge and technological capabilities and to innovate. (Sawhney &
Prandelli, 2000; Howells, James & Malik, 2003). Industries and businesses dom-inated by information and knowledge are early adopters of distributed innova-tion. However, already ten years ago, it was expected to expand into a multi-tude of other domains. (Lakhani & Panetta, 2007). Innovation opportunities are distributed in the corporate environment. Technological advancements and market disruptions are major drivers for companies to pursue distributed inno-vation opportunities. (West & Bogers, 2017).
2.3.1 Distributed innovation systems
Distributed innovation is strongly connected with mechanisms and dynamics of knowledge co-creation and sharing (Sawhney & Prandelli, 2000; Howells, James
& Malik, 2003). Distributed innovation systems are characterized by decentral-ized problem solving, self-selected participation, self-organizational coordina-tion and collaboracoordina-tion, free access to knowledge, and hybrid organizacoordina-tional structures that combine commercial and community success. Open source software communities are the best-known example of a distributed innovation system. (Lakhani & Panetta, 2007). However, distribution increases the com-plexity of innovation systems (Howells, James & Malik, 2003).
Traditional vertically integrated innovation system is linear. Innovations start with academic knowledge, continue with firm’s internal development pro-cesses and finally to commercialization attempts into market. There are four phases in the vertically integrated innovation path: 1) basic and applied
re-search, 2) invention, 3) development, and 4) production. The company carries out all activities from transforming research knowledge into commercially rele-vant inventions, developing them into marketable innovations, and distributing them to market. The centralized innovation model favors large enterprises due to their vast resources and capabilities. However, smaller organizations lack assets and control power and thus face challenges with the innovation model.
(Bogers & West, 2012). Also, Sawhney and Prandelli (2000) pit distributed inno-vation systems against the traditional closed innoinno-vation systems. Yet, there are some similarities with open innovation and traditional vertically integrated in-novation. These include the firm as the unit of analysis and interest in profit and economies of scale. Yet, the mode and locus of innovation is critically dif-ferent in the two models and attitude towards external innovators and knowledge spillover are the opposite. (Bogers & West, 2012).
According to Bogers and West (2012), distributed innovation encompasses open innovation, which is the firm-centric aspect of distributed innovation. The user-centric aspect of distributed innovation is known as user innovation. Even if this study focuses on the firm-centric innovation the concept of user innova-tion can provide interesting viewpoints. User communities, like developer and open source communities, could provide valuable inbound knowledge flows (Bogers & West, 2012). Moreover, Lakhani and Panetta (2007) mention the im-portance of open source software communities in studying distributed innova-tion. The literature confirms distributed and open innovation are related con-cepts and the two innovation systems share characteristics. However, they have a different focus on the phenomenon and its mechanisms. This study focuses on the firm-centric aspect of distributed innovation. However, user innovation is not excluded.
2.3.2 Distributed innovation management
The outcomes in distributed innovation are unpredictable and multiple hetero-genous actors contribute towards them. Each actor can have a different motiva-tion and objective for innovamotiva-tion. Moreover, they can have different capabilities and resources as well. (Nambisan et al., 2017; Lakhani & Panetta, 2007). The motivation for innovation and related decision-making and objectives could be divergent even within the firm. Constant evaluation of firm’s own and its part-ners’ (i.e. innovation network) competencies is required for exploitation of dis-tributed innovation and for the related risk management. (Howells, James &
Malik, 2003).
The boundary crossing nature of distributed innovation sets requirements also for innovation governance, management, and architectures. They need to tolerate and foster decentralized innovation ecosystems and processes. Digital innovation and development of digitalized products and services are converg-ing in a sense. The models of development and innovation are both becomconverg-ing increasingly distributed. Some of the tools for coordination, control, and facili-tation, such as platform boundary resources, are also similar. (Yoo et al., 2010).
Digitalization and distributed innovation also increase complexity that needs to be managed. New kinds of socio-cognitive sensemaking, orchestration, integra-tion, and continuous solution-problem matching are needed. (Nambisan et al., 2017). Successful utilization of distributed innovation provides multiple techno-logical routes for innovation. However, coordination and management are needed for cohesive and aligned innovation processes and outcomes. Otherwise, there is a risk of divergence and fragmentation. (Howells, James & Malik, 2003).
Different timeframes and horizons need to be considered in distributed innovation management. The objectives and expectations of knowledge acquisi-tion, partner relationships, types of knowledge, functional focus, and risks are different in short- and long-term scope of distributed innovation. Collaboration in both timeframes can be focused on partnerships or technologies. (Howells, James & Malik, 2003).
Short-term collaboration is focused around specific outcomes on products and processes and is often contract-based. Therefore, short-term collaboration is referred to as problem-oriented innovation. Uncertainty and risks are typically low. However, the impact of failure could still be high. The innovation timeframe increases with reciprocal collaboration that can include informal and non-contractual cooperation between different organizations. Joint ventures and other ownership-based collaboration spans even longer time horizon.
Ownership-based collaboration opens new kinds of opportunities, such as technology insourcing. In long-term collaboration uncertainty and risks tend to increase and are generally high. Alignment with future markets and competen-cies is important in long-term distributed innovation. (Howells, James & Malik, 2003).
Business models for distributed innovation must consider how actors out-side their organizational boundaries can be motivated and involved in innova-tion processes, and how value could be captured. An example of contribuinnova-tion motivation can be drawn from open source development communities. The contributor, i.e. external innovator, expects to benefit from the contribution in future. However, a business or technical need is often required to contribute in the first place. In user communities, there are also personal reasons to ute to distributed innovation. For example, a software developer could contrib-ute for personal reputation, skills development, learning, job market signaling, or satisfaction and entertainment. The cost and effort to participate in distribut-ed innovation must be low to decrease the barriers to entry and to increase the diversity and number of contributors. (Lakhani & Panetta, 2007).
Distributed innovation calls for openness, collaboration, and knowledge sharing. In addition, intellectual property policies need to be aligned with the principles of open and distributed innovation. However, the level of openness needs to be negotiated and tuned. (Lakhani & Panetta, 2007). However, there are shades and fine-grained levels between open and closed innovation systems and models (Sawhney & Prandelli, 2000).
Inbound and outbound knowledge flows require different kind of capabil-ities for value capture. Inbound knowledge flows call for internal capabilcapabil-ities,
such as knowledge absorption, to capture value from external innovation sources. Stakeholders and communities can help to discover innovation sources, but the firm itself needs to able to internalize the knowledge. On the other hand, outbound knowledge flows require balancing between controlling and empow-ering innovation. Value capture relies on intellectual property protection and monetization of external use. For instance, licensing can be used to project con-trol on outbound knowledge flows. However, strong intellectual property pro-tection is likely to be detrimental to distributed innovation mechanisms.
(Bogerst & West, 2012).
Sawhney and Prandelli (2000) claim managing distributed innovation is constant balancing and governance between order and chaos. They propose a governance mechanism called community of creation that balances between closed hierarchical innovation model and open market-based innovation model.
Distributed innovation management requires structure to control chaos and coordination mechanisms for knowledge creation but also freedom and open-ness to trade and access knowledge. Community of creation is based on transac-tion cost theory, community management, intellectual property rights analysis, and complexity theory. (Sawhney & Prandelli, 2000). The literature implies that distributed innovation and its management are complex topics. Therefore, they core ideas of distributed innovation are covered in this study, but the phenom-enon is not discussed or presented in detail.
Finally, it should be noted that distributed innovation is not a replacement for in-house innovation. Rather, it expands and complements it. (Lakhani &
Panetta, 2007; Howells, James & Malik, 2003). Distributed innovation is unpre-dictable and cannot deliver on-demand outcomes. Aligning business models with open and distributed innovation can be challenging. For instance, many open source projects fail in a commercial sense. Openness requires a transfor-mation of intellectual property protection and innovation models. There are both real and imaginary risks in relinquishing control and decreasing secrecy regarding innovation. (Lakhani & Panetta, 2007). There is also a risk in over ex-panding outsourcing of knowledge and technological capabilities. It can lead to weakened technology and innovation capabilities, core competencies, and knowledge absorption capabilities within the firm. Vendor locks in technology and partners should be avoided to maintain flexibility. It should also be noted that successful management and exploitation of distributed innovation is hard-er than of the traditional centralized innovation. (Howells, James & Malik, 2003).
3 DIGITAL PLATFORM LITERATURE
This section reviews literature on digital platforms and digital platform innova-tion. The concept of boundary resource is defined and its connection with digi-tal platform innovation is described. API is defined and presented as a type of platform boundary resource. Finally, the roles and influence of APIs in digital platform innovation is explored.